1. Recommendation for working out a new soil ranking system based on the results of the SOILMAP project László Manczinger Department of Microbiology, Faculty of Science and Informatics, University of Szeged, Hungary László Manczinger, Isidora Radulov, Adina Berbecea, Enikő Sajben-Nagy, Andrea Palágyi, Dorin Tărău, Lucian Dumitru Niţă, Csaba Vágvölgyi

2. HU-1 Intensive wheat culture (Öthalom) HU-2 Forest (Kiszombor) HU-3 Intensive wheat culture (Kiszombor) HU-4 Meadow (Kiszombor) HU-5 Bio-wheat (Kiszombor) HU-6 Intensive wheat culture (Szentes) HU-7 Intensive wheat culture (Sándorfalva) HU-8 Intensive wheat culture (Derekegyház) HU-9 Intensive wheat culture (Újszeged) HU-10 Intensive wheat culture (Makó) RO-1 Bio-wheat culture (Cenad) RO-2 Intensive wheat culture ICAR (Cenad) RO-3 Meadow (Cenad) RO-4 Forest (Cenad) RO-5 Intensive wheat culture (Sânnicolau Mare) RO-6 Intensive wheat culture (Sânnicolau Mare) RO-7 Intensive wheat culture (Lovrin) RO-8 Intensive wheat culture (Clarii) RO-9 Intensive wheat culture (Săcălaz – Beregsana) RO-10 Intensive wheat culture (COMAGRA) Two type sampling from every places A: upper layer : 0-20 cm B: lower layer : 20-40 cm SAMPLING TIMES SPRING - March SUMMER- August AUTUMN- November SAMPLING PLACES

3. 6 8 3 7 5 1 4 9 2 10 7 2 4 5 6 9 8 3 1 Two type sampling from every places A: upper layer : 0-20 cm B: lower layer : 20-40 cm The sampling places on the map of the region

4. The investigated parameters of the soil samples Rough sand ( 2,0 - 0,2 mm) Fine sand ( 0,2 –0,02 mm ) Dust ( 0,02 – 0,002 mm ) Colloid clay ( sub 0,002 mm ) Physical clay ( sub 0,01 mm ) pH in water Carbonate ( CaCO 3 ) Humus Phosphorus mobile ( P mobile ) recalc pH Potassium mobile (K mobile ) Zinc Copper Manganese Nickel Cadmium Iron Lead Physical-chemical parameters Biochemical parameters Microbiological parameters E1Phosphatase E2β-glucosidase E3Cellobiohydrolase E4 β-xylosidase E5Trypsin-like protease E6Chymotrypsin-like protease E7Palmitoyl-esterase E8Chitinase 1.Species richness of bacteria 2. Species richness of fungi 3. Diversity of important bacterial genera 4. Diversity of toxinogenic fungi

5. Some important results regarding the physical-chemical parameters, processed with Excel and OpenStat softwares pH

6. Some important results regarding the physical-chemical parameters Humus

7. Some important results regarding the physical-chemical parameters (phosphorus and potassium) In the Romanian soils the amount of both P and K is frequently much more less in the lower layer than in the upper layer.

8. Some important results regarding the physical-chemical parameters (cadmium and copper)

9. Some important results regarding the physical-chemical parameters (zinc and lead)

10. Some important results regarding the physical-chemical parameters (manganese and iron)

11. Regression analysises X versus Y Plot X = VAR1, Y = VAR2 from file: Temporary.TEX Variable Mean Variance Std.Dev. VAR1 89.31 1923.73 43.86 VAR2 295.80 19081.85 138.14 Correlation = 0.7066, Slope = 2.23, Intercept = 97.03 Standard Error of Estimate = 97.74 Number of good cases = 20 P-mobile – K-mobile regression in the Hungarian soil samples

12. X versus Y Plot X = VAR1, Y = VAR2 from file: Temporary.TEX Variable Mean Variance Std.Dev. VAR1 58.88 2031.53 45.07 VAR2 262.85 45963.82 214.39 Correlation = 0.5739, Slope = 2.73, Intercept = 102.10 Standard Error of Estimate = 175.57 Number of good cases = 20 P-mobile – K-mobile regression in the Romanian soil samples

13. Multiple regression of heavy metals in the Romanian samples Variables Cu Mn Ni Cd Pb Zn Cu 1.000 -0.311 0.031 0.411 0.453 0.488 Mn -0.311 1.000 0.225 0.371 0.099 -0.279 Ni 0.031 0.225 1.000 0.301 0.359 0.235 Cd 0.411 0.371 0.301 1.000 0.674 0.163 Pb 0.453 0.099 0.359 0.674 1.000 0.259 Zn 0.488 -0.279 0.235 0.163 0.259 1.000 Correlation matrix

14. Multiple regression of heavy metals in the Hungarian samples Variables Cu Mn Ni Cd Pb Zn Cu 1.000 0.323 -0.091 0.279 0.418 -0.006 Mn 0.323 1.000 -0.395 -0.235 -0.336 -0.551 Ni -0.091 -0.395 1.000 -0.174 0.315 0.706 Cd 0.279 -0.235 -0.174 1.000 0.544 0.119 Pb 0.418 -0.336 0.315 0.544 1.000 0.269 Zn -0.006 -0.551 0.706 0.119 0.269 1.000 Correlation matrix

15. Analysis of soil enzyme data

16. We worked on microtiter plates with chromogenic substrates A Phosphatase B β-glucosidase C Cellobiohydrolase D β-xylosidase E Trypsin-like protease F Chymotrypsin-like protease G Palmitoylesterase H Chitinase

17. Relative activities of soil enzymes in the spring and summer sample series. Hungarian soils, upper layer. SPRING SUMMER The other sample series showed very like pictures. The summer samples, as being most diverse, were statistically analysed and used for soil qualifying.

18. Soil type – soil enzyme correlations calculated with OpenStat software HU-Enzyme-Lower soil layer X VERSUS MULTIPLE Y VALUES PLOT X= VAR1: 1=non fertilized soils, 2= fertilized soils CORRELATION MATRIX Correlations VAR2 VAR3 VAR4 VAR5 VAR6 VAR7 VAR2 1.000 -0.593 -0.407 -0.081 0.039 0.363 VAR3 -0.593 1.000 0.617 0.261 0.483 0.032 VAR4 -0.407 0.617 1.000 0.717 -0.024 -0.095 VAR5 -0.081 0.261 0.717 1.000 0.136 0.309 VAR6 0.039 0.483 -0.024 0.136 1.000 0.821 VAR7 0.363 0.032 -0.095 0.309 0.821 1.000 VAR8 -0.717 0.414 -0.026 -0.402 0.111 -0.339 VAR9 0.379 -0.354 0.038 0.534 -0.217 0.042 VAR1 0.403 0.158 0.142 0.239 0.194 0.264 Correlations VAR8 VAR9 VAR1 VAR2 -0.717 0.379 0.403 VAR3 0.414 -0.354 0.158 VAR4 -0.026 0.038 0.142 VAR5 -0.402 0.534 0.239 VAR6 0.111 -0.217 0.194 VAR7 -0.339 0.042 0.264 VAR8 1.000 -0.475 -0.536 VAR9 -0.475 1.000 0.237 VAR1 -0.536 0.237 1.000 A Phosphatase VAR2 B β-glucosidase VAR3 C Cellobiohydrolase VAR4 D β-xylosidase VAR5 E Trypsin-like protease VAR6 F Chymotrypsin-like protease VAR7 G Palmitoylesterase VAR8 H Chitinase VAR9 We made the corre- lation matrices in every soil sample series

19. The enzyme activities in the lower layers of intensively cultivated Hungarian soils are higher, except of palmitoylesterase (G). A Phosphatase VAR2 B β-glucosidase VAR3 C Cellobiohydrolase VAR4 D β-xylosidase VAR5 E Trypsin-like protease VAR6 F Chymotrypsin-like protease VAR7 G Palmitoylesterase VAR8 H Chitinase VAR9 Correlation of soil enzyme activities with the use of fertilizers and pesticides

20. A Phosphatase VAR2 B β-glucosidase VAR3 C Cellobiohydrolase VAR4 D β-xylosidase VAR5 E Trypsin-like protease VAR6 F Chymotrypsin-like protease VAR7 G Palmitoylesterase VAR8 H Chitinase VAR9 The enzyme activities in the upper layers of fertilizer and pesticide treated Hungarian soils are frequently higher, than in the soils of nonintensive fields (forest, meadow, biocultivation). Correlation of soil enzyme activities with the use of fertilizers and pesticides

21. A Phosphatase VAR2 B β-glucosidase VAR3 C Cellobiohydrolase VAR4 D β-xylosidase VAR5 E Trypsin-like protease VAR6 F Chymotrypsin-like protease VAR7 G Palmitoylesterase VAR8 H Chitinase VAR9 In the Romanian soils all enzyme activities were strongly less in the intensively cultivated fields both in the upper and lower layers exept of phosphatase. Correlation of soil enzyme activities with the use of fertilizers and pesticides

22. Determination of soil microbial diversity

23. The new molecular diversity methods -DGGE = Denaturing Gradient Gelelectrophoresis -TGGE = Temperature Gradient Gelelectrophoresis -TTGE= Temporal Temperature Gradient gelelectrophoresis -SSCP= Single Strand Conformational Polymorphism -RISA, ARISA (Automated) Ribosomal Intergenic Spacer Analysis -Community ARDRA, Community ITS RFLP - T-RFLP= Terminal Restriction Fragment Length Polymorphism

24. RISA Ribosomal Intergenic Spacer Analysis

25. Variability of the size of the ITS region in distinct bacterial groups

26. Variability of the size of the ITS region in distinct fungal groups

27. Multiplication of the ITS region and electrophoresis of the PCR products PCR was carried out in a final volume of 50 μl containing 5 μl of Taq polymerase 10x puffer, 1.6 mM MgCl2, 200 μM for each of the dNTPs, 10 pM primers, 5 μl of template DNA (app. 100 ng) in distilled water and 1 U Taq DNA polymerase (Fermentas). The PCR product was visualized with gelelectophoresis, and the DNA fragments in the gels were stained with SYBR Green and analyzed under UV light. Primers used in bacteria: For the amplification of the bacterial ITS region, the Eub-ITSF as forward and Eub-ITSR as reverse primers were used. Eub-ITSF: 5’-GTCGTAACAAGGTAGCCGTA-3’ Eub-ITSR: 5’- GCCAAGGCATCCACC-3’ Primers used in fungi: the best is the ITS5 –forward ITS4-reverse combination. ITS5: 5’-GGAAGTAAAAGTCGTAACAAGG-3’ ITS4: 5’-TCCTCCGCTTATTGATATGC-3’

28. Some results obtained with the SOILMAP samples M 1/1 1/2 2/1 2/2 3/1 3/2 4/1 4/2 5/1 5/2 6/1 6/2 7/1 7/2 8/1 8/2 9/1 9/2 Bacterial RISA fingerprints of Romanian soil samples

29. Some results obtained with the SOILMAP samples M 1/1 1/2 2/1 2/2 3/1 3/2 4/1 4/2 5/1 5/2 6/1 6/2 7/1 7/2 8/1 8/2 9/1 9/2 Bacterial RISA fingerprints of Hungarian soil samples The fingerprints were very peculiar to the given sample collecting places and there was no significant distinction between the upper and lower layers of the same sampling place.

30. M 1/1 1/2 2/1 2/2 3/1 3/2 4/1 4/2 5/1 5/2 6/1 6/2 7/1 7/2 8/1 8/2 9/1 9/2 Fungal RISA fingerprints of Romanian soil samples made with ITS5-ITS4 primer pair. 1/1 1/2 2/1 2/2 3/1 3/2 4/1 4/2 5/1 5/2 6/1 6/2 7/1 7/2 8/1 8/2 9/1 9/2 Fungal RISA fingerprints of Hungarian soil samples made with ITS5-ITS4 primer pair.

31. As the fungal fingerprints were not enough diverse we used for soil qualifying the bacterial fingerprints only. M 1/1 1/2 2/1 2/2 3/1 3/2 4/1 4/2 5/1 5/2 6/1 6/2 7/1 7/2 8/1 8/2 9/1 9/2 Bacterial RISA fingerprints of Hungarian soil samples Pf Ba Str Bs 200 100 Forest Meadow

32. Correlation analysis with the bacterial species richness values RISA fingerprints, summer bacterial species richness, RO+HU+upper+lower X VERSUS MULTIPLE Y VALUES PLOT WITH OPENSTAT SOFTWARE CORRELATION MATRIX Correlations VAR2 VAR3 VAR1 VAR2 1.000 0.351 -0.249 UPPER VAR3 0.351 1.000 -0.642 LOWER VAR1 -0.249 -0.642 1.000 VAR1=1 Non intenzively cultivated soils VAR1=2 Intenzívely cultivated soils Means Variables VAR2 VAR3 VAR1 11.950 14.550 1.700 Standard Deviations Variables VAR2 VAR3 VAR1 8.224 8.841 0.470 No. of valid cases = 20

33. 1.RISA-UPPER-HU 0-20 cm VAR1=1 Non intenzively cultivated soils VAR1=2 Intenzívely cultivated soils

34. RISA-LOWER-HU 20-40 cm VAR1=1 Non intenzively cultivated soils VAR1=2 Intenzívely cultivated soils

35. The synthesis of the data: establishment a new complex soil qualifying system Six positive and six negative soil parameters were selected from the summer collected soil samples: Trypsin-like protease+40 Palmitoylesterase (PE)+40 Bacterial species richness (SR)+40 Humus+40 Phosphorus, mobile (P mobile )+40 Potassium, mobile (K mobile )+40 Zinc-40 Copper-40 Manganese-40 Nickel-40 Cadmium-40 Lead-40 The maxima of + parameters get +40 „soil value points” The maxima of negative ones ( the heavy metals) get -40. All measured parameters had been proportioned to these +40, -40 values, after that the soil value points were summed in all cases of samples.

37. The quality values of Hungarian and Romanian soils A: 0-20 cm, B. 20-40 cm Forest soils: HU2 and RO4

38. 6 8 3 7 5 1 4 9 2 10 7 2 4 5 6 9 8 3 1 = above +40 = 0- +40 = 0- -40 = below -40 0-20 cm The worst soils are besides the road and railway of Szeged-Makó.

39. 6 8 3 7 5 1 4 9 2 10 7 2 4 5 6 9 8 3 1 = above +40 = 0- +40 = 0- -40 = below -40 20-40 cm

40. The averaged quality values of Hungarian and Romanian soils

41. 6 8 3 7 5 1 4 9 2 10 7 2 4 5 6 9 8 3 1 = above +40 = 0- +40 = 0- -40 = below -40 Averaged

42. Thank you for your attention!